class_docstr: Test MLlib algorithms on plain lists, to make sure they're passed through
  as NumPy arrays.
code: def test_gmm(self):
      from pyspark.mllib.clustering import GaussianMixture
  
      data = self.sc.parallelize(
          [
              [1, 2],
              [8, 9],
              [-4, -3],
              [-6, -7],
          ]
      )
      clusters = GaussianMixture.train(
          data,
          2,
          convergenceTol=0.001,
          maxIterations=10,
          seed=1,
      )
      labels = clusters.predict(data).collect()
      self.assertEqual(labels[0], labels[1])
      self.assertEqual(labels[2], labels[3])
  
imports: import os
  import tempfile
  from shutil import rmtree
  import unittest
  from numpy import array, array_equal
  from py4j.protocol import Py4JJavaError
  from pyspark.mllib.fpm import FPGrowth
  from pyspark.mllib.recommendation import Rating
  from pyspark.mllib.regression import LabeledPoint
  from pyspark.serializers import CPickleSerializer
  from pyspark.testing.mllibutils import MLlibTestCase
  from pyspark.testing.utils import eventually
label_desc: Use image node_id 4 for calling the ListTests obj's underlying member method code with example usage: obj.test_gmm() without return types